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Event Type
Research Presentation
Academic Department
Mathematics and Statistics
Start Date
5-4-2021 12:00 AM
Description
Gross Domestic Product or GDP is seen as the most conventional and comprehensive way of measuring the economic growth of a country. This paper, with 20 different indicators, showcases a data driven forecasting model of nominal GDP of the country U.S. The indicators are chosen within the aspects of business, trade, market, health, labor, government and prices and integrates the lagging, leading and coincident indicators. We will be using the concept of machine learning and creating testing and training set to create linear regression model and show how accurate the model is in predicting the nominal GDP of the country. The model will determine the nominal GDP of the U.S for the coming 10 years.
Gross Domestic Product Forecasting Model
Gross Domestic Product or GDP is seen as the most conventional and comprehensive way of measuring the economic growth of a country. This paper, with 20 different indicators, showcases a data driven forecasting model of nominal GDP of the country U.S. The indicators are chosen within the aspects of business, trade, market, health, labor, government and prices and integrates the lagging, leading and coincident indicators. We will be using the concept of machine learning and creating testing and training set to create linear regression model and show how accurate the model is in predicting the nominal GDP of the country. The model will determine the nominal GDP of the U.S for the coming 10 years.
Comments
Under the direction of Dr. Giancarlo Schrementi.